Exercise intensity of the upper limb can be enhanced using a virtual rehabilitation system
Bibliographic record
Abstract
PURPOSE: Motor recovery of the upper limb (UL) is related to exercise intensity, defined as movement repetitions divided by minutes in active therapy, and task difficulty. However, the degree to which UL training in virtual reality (VR) applications deliver intense and challenging exercise and whether these factors are considered in different centres for people with different sensorimotor impairment levels is not evidenced. We determined if (1) a VR programme can deliver high UL exercise intensity in people with sub-acute stroke across different environments and (2) exercise intensity and difficulty differed among patients with different levels of UL sensorimotor impairment. METHODS: Participants with sub-acute stroke (<6 months) with Fugl-Meyer scores ranging from 14 to 57, completed 10 ∼ 50-min UL training sessions using three unilateral and one bilateral VR activity over 2 weeks in centres located in three countries. Training time, number of movement repetitions, and success rates were extracted from game activity logs. Exercise intensity was calculated for each participant, related to UL impairment, and compared between centres. RESULTS: Exercise intensity was high and was progressed similarly in all centres. Participants had most difficulty with bilateral and lateral reaching activities. Exercise intensity was not, while success rate of only one unilateral activity was related to UL severity. CONCLUSION: The level of intensity attained with this VR exercise programme was higher than that reported in current stroke therapy practice. Although progression through different activity levels was similar between centres, clearer guidelines for exercise progression should be provided by the VR application.Implications for rehabilitationVR rehabilitation systems can be used to deliver intensive exercise programmes.VR rehabilitation systems need to be designed with measurable progressions through difficulty levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".